Enterprise AI Analysis
Demystifying LLM-Based Software Engineering Agents
Recent advancements in large language models (LLMs) have significantly advanced the automation of software development tasks, including code synthesis, program repair, and test generation. More recently, researchers and industry practitioners have developed various autonomous LLM agents to perform end-to-end software development tasks. These agents are equipped with the ability to use tools, run commands, observe feedback from the environment, and plan for future actions. However, the complexity of these agent-based approaches, together with the limited abilities of current LLMs, raises the following question: Do we really have to employ complex autonomous software agents?
Executive Impact: Key Performance Metrics
Our analysis reveals quantifiable improvements across critical business functions.
% Resolved
Highest Performance Among Open-Source Agents
$0.70
Avg. Cost Per Fix
78,165
Avg. Input/Output Tokens
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Our agentless approach to automatically solve software development problems. AGENTLESS leverages LLM-empowered prompting-based and embedding-based retrieval to perform hierarchical localization. During repair, AGENTLESS samples multiple candidate patches in a simple diff format for efficient patch generation. Agentless then generates reproduction tests to verify that the issue has been resolved. Finally, AGENTLESS leverages both regression tests and generated reproduction tests to select the final submission patch.
32%
Highest Performance Among Open-Source Agents
Agentless Software Development Process
Localization
→
Repair
→
Patch Validation
We performed manual classifications on the problems in the popular SWE-bench Lite dataset. We found that there are problematic problems with unclear, misleading issue descriptions, as well as problems that contain exact ground truth patches. To address these issues, we constructed a filtered dataset of SWE-bench Lite-S that excludes such problematic issues, enabling more rigorous evaluation and comparison. This has also been confirmed recently by OpenAI, which acknowledged our benchmark and released SWE-bench Verified along the same direction.
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OpenAI Adoption: GPT-4o & o1 Models
AGENTLESS has been adopted by OpenAI as the go-to approach to showcase the real-world coding performance of both GPT-4o and the new o1 models. This underscores the industry recognition of its simplistic yet effective design.
50%+
Solve Rate with Claude 3.5 Sonnet on SWE-bench Verified
Industry Standard
For Real-World Coding Performance
Calculate Your Potential ROI with Agentless AI
Estimate the cost savings and reclaimed developer hours your organization could achieve.
Estimated Annual Savings
$0
Reclaimed Developer Hours
0
Our Proven Implementation Roadmap
A structured approach to integrating Agentless AI into your software development lifecycle.
Phase 1: Discovery & Assessment
Understand your current workflows and identify key integration points for Agentless AI.
Phase 2: Customization & Integration
Tailor Agentless to your specific codebase and development environment.
Phase 3: Pilot & Iteration
Run Agentless on a subset of issues, gather feedback, and fine-tune performance.
Phase 4: Full Scale Deployment
Deploy Agentless across your organization for autonomous software development.
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Let's explore how Agentless AI can streamline your development, reduce costs, and accelerate innovation.
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